The Opinion of The Taiwanese Media on Pro-Independence and Pro-Unification with People Republic China During Tsai Ing-Wen Era

Abstract: Taiwan-People’s Republic of China (PRC) relations have attracted a lot of attention in recent years. These relationships are at the core of Taiwan's social and political life. The pro-independence and pro-unification social cleavage line affected by Taiwan-PRC relations divides Taiwan's political life into two separate camps. These camps are pan-blue coalition (pro-unification) and the pan-green coalition (pro-independence). In this study, the views of Taiwanese newspapers containing both camps on Taiwan-PRC relations are analyzed quantitatively. The study focuses specifically on the Tsai Ing-Wen period. Within the scope of the study, dictionary-based and sentiment analysis methods are used and the selected newspapers are examined with these principles. As a consequence of the quantitative analysis of these three English Taiwanese newspapers, it is shown that the media adopted a perspective similar to the views of the government and the DPP throughout the Tsai Ing-Wen period. Furthermore, sentiment analysis is done of the news from these newspapers relevant to PRC-Taiwan ties. It has been noted that the reporting regarding PRC-Taiwan ties in all three newspapers has a positive language.
Keywords: Taiwan, PRC, Text Analysis, Pro-Unification, Pro-Independence, KMT, DPP


RELATED LINK: http://daadtr.com/DAAD/ArchiveIssues/PDF/787eb20a-e1bb-ea11-810a-005056b0673e

2020 General Election in Taiwan

The results of the Taiwan general elections had been eagerly awaited for a long time. Especially the results of the local elections in 2018 made these general elections more remarkable. In addition, the protests in Hong Kong increased the importance of the Taiwan general elections.

In this article, I will first describe my observations in general elections. Then I will briefly explain the results of Twitter analysis about Taiwan general election, which I analyzed with the text analysis method.

OBSERVATION

In the morning, I started following the general elections in Taiwan. At the same time, I also tried to feel the political atmosphere inside country. Because of that reason, I talked with some of my Taiwanese friends. Around 7 PM, the election results began to be announced. Around 7.30pm, I first went to the KMT's headquarter in Kaohsiung. However, the KMT headquarter was quite empty. The most important reason for this was that DPP was ahead by far, according to the first results. Another important reason was that pan-blue party sympathizers did not trust their candidates.

Around 8 pm, the results began to be clear and I decided to go to the DPP's rally. The rally area was quite crowded. It was as crowded as the KMT's rally, which won last year's local elections. The excitement and joy of people in the rally area was quite interesting.

As a result of the local elections in 2018, the Green Party(DPP) lost the local elections. As a result of the defeat, the DPP leader and Taiwanese president Tsai Ing-Wen resigned from party leadership. But Tsai Ing-Wen, who withdrew her resignation, won this year's general elections.

With the results, the KMT candidate Han Kuo-yu made his speech first. About an hour later, Tsai Ing wen, the winner of the election, made her speech. However, the first thing that caught my attention during this speech was that Tsai Ing Wen spoke quite calmly and seriously. Unlike Ing Wen, DPP sympathizers were extremely happy.

In her speech, Ing wen praised Taiwan's democracy and stated that relations with China will continue in the same way.

TWITTER ANALYSIS

2000 English tweets about Taiwanese general elections between the January 1st and January 20th were examined with R computer program. First, the most commonly used words were found in these 2000 tweets. The words Economy, Growth, Hong Kong Protests, independence and victory are the most used words in 2000 tweets.

Most of the tweets are positive about Ing-Wen and DPP's victory.

Words like freedom, peaceful, democracy, growth and hope are the most commonly used positive words. Words such as infrared, protest, fight and Xi Jinping are the most commonly used negative words. 

Secondly, sentiment analysis of these 2000 tweets was analyzed. The highest sentiment in these tweets is trust. Then comes anticipation and fear.

Words like president, democracy, freedom, integrity, alliance and brilliant are the words with the highest trust sentiment. Words like young, vote, result and prevention are the words with the highest sense of anticipation. Words like Fight, Interfere, and Xi Jinping are the words with the highest sense of fear.

CONCLUSION

In conclusion, DPP sympathizers were very happy and hopeful at the DPP rally. Twitter analysis results gave similar results.

However, despite all this happiness and anticipation, it is a matter of curiosity how China-Taiwan relations and Ing-Wen's attitude towards China will change and shape.

Lijphart's Democracy Idea and Turkey

This paper tries to apply Lijphart’s way of description with ten factors for the two models of democracy to the Republic of Turkey. Turkey, with her important location, which is a country with a different political structure and social structure, can be added to Lijphart example. This research mainly analysis Turkish political system with Lijphart’s majoritarian and consensus democracy idea. In this study, while examining the case of Turkey, the World Bank, IMF, and IV project, Freedom House reports and data were used.


URL: https://transformative.ub.ac.id/index.php/jtr/article/view/46

Trend Topic Analysis with R

Twitter is one of the most popular social media applications in recent years. With the R program, it is possible to analyze many data from Twitter. Two of these analyzes are sentiment and polarity analysis.

These analyzes were applied to one of the trend topics on Twitter. For this purpose, the first trend topics in the world were found with "getTrend ()" function. As a results of this research, the most popular trend topic is #gretathunberg.

Afterwards, tweets were cleaned and sentiment and polarity analysis were performed.

First, polarity analysis was performed. As a results of this analysis, the majority of tweets related to #gretathumberg are positive.

Figure 1: polarity analysis

Secondly, sentiment analysis was performed. According to this analysis, anticipation and trust are the most dominant feelings among tweets.

Figure 2: Sentiment analysis (second column is anticipation.)

Finally, the most popular devices, which are used to post on twitter, were analyzed. Twitter web app and twitter android are the most used software.

Figure 3: Most used software and devices

Terrorism Dilemma

Terrorism has been one of the most remarkable issues in recent years. Many scholars argue about terrorism. However, the lack of a definition of terrorism complicates the issue.

The most acceptable definition of terror in the world is the CIA's definition of terror. This study uses the CIA's definition of terrorism. According to Central Intelligence Agency (2007), terrorism means premeditated, politically motivated violence perpetrated against noncombatant targets by subnational groups or clandestine agents, usually intended to influence an audience.

This definition is quite clear. Based on this definition, the YPG in Syria is a terrorist organization. However, many Western countries and the United States do not recognize YPG as terrorists. But this organization has caused the deaths of many innocent people in Turkey. Nevertheless, the media of many countries continue to have positive news about this organization. In addition, there are also positive news about the terrorist organization on Twitter.

For this purpose, sentiment and polarity analysis related with YPG in CNN, The New York Times and Twitter was performed.

                       

      

                                      Figure 1: Polarity result of CNN

Firstly, CNN's latest news about ypg is examined. The analysis revealed that the CNN's news was very positive about YPG. The most commonly used sentiment in the news about YPG is trust.

                               

                                          Figure 2: Sentiment result of CNN

            Secondly, the last news of the new york times about ypg was analyzed. Similar results were obtained with CNN. The new york times ypg news is also very positive. The most commonly used sentiment in the New York Times’ news about YPG is trust.

                                                Figure 3: Polarity result of New York Times

                                               Figure 4: Sentiment result of New York Times

Finally, the last 100 tweets about YPG on Twitter were analyzed. Twitter also has positive tweets about YPG, like CNN and New York Time. The most commonly used sentiment in twitter about YPG is fear. Tweets about ISIS raise the fear rate. That’s why fear was the most common sentiment in this anaysis.

                                      Figure 5: Polarity result of Tweets

                                      Figure 6: Sentiment result of Tweets

As seen above, there are positive views on YPG, which many countries recognize as terrorist organizations, in the media and social media. 

Cryptocurrency(1)

           In recent years, cryptocurrencies have become quite popular in the world. However, this issue of cryptocurrency reliability is highly controversial.

            In addition cryptocurrency is used in many illegal trade on the deep web. These cryptocurrencies are used for illegal slave trade and drug.

            In addition to all these problems, one of the most important problems is why the cryptocurrency rises and falls. Many scholars have different opinions on this issue. However, the common view is that the ups and downs in bitcoin or cryptocurrencies do not depend on any physical factors.

            However, I think that this idea is not correct. I think that real economic activities play an important role in the rise and fall of bitcoin and other cryptocurrencies. I prepared a data set to test my idea. This data set includes data such as ripple and bitcoin. In addition, this data set includes data such as oil, dollar and gold.

           This data set covers June 2014 to August 2019. The values in this dataset were first translated into Turkish Lira and then normalized in R program. My results were different from the literature.

           Firstly, correlation analysis was performed. According to the results of the correlation analysis, there is a strong correlation between Bitcoin and USD and gold. In addition, there is also a correlation between oil and bitcoin. But this correlation is not strong like USD and gold.

           In addition to correlation analysis, linear regression analysis was performed. The results of linear regression analysis confirmed the results of correlation analysis. In linear regression analysis, USD was chosen as the dependent variable. Bitcoin was chosen as an independent variable.

           Linear regression analysis also shows that there is a significant relationship between Bitcoin and USD.

Protests in Hong Kong

Hong Kong is definitely different from other Chinese cities. Hong Kong was a British colony for more than 150 years - part of it, Hong Kong island, was ceded to the UK after a war in 1842. 

The territory was also popular with migrants and dissidents fleeing instability, poverty or persecution in mainland China.

Then, in the early 1980s, as the deadline for the 99-year-lease approached, Britain and China began talks on the future of Hong Kong - with the communist government in China arguing that all of Hong Kong should be returned to Chinese rule.

The two sides reached a deal in 1984 that would see Hong Kong return to China in 1997, under the principle of "one country, two systems".

As a result, Hong Kong has its own legal system and borders, and rights including freedom of assembly and free speech are protected.

However, the country is currently on the agenda with great uprisings. Protests have continued in Hong Kong since April. The reason for these protests is the act adopted by the Hong Kong parliament. This act would have allowed extradition from Hong Kong to mainland China. 

The adoption of this law attracted the reaction of many people, especially young people. This reaction quickly turned into protests.

Western media soon began to follow the protests closely. Almost every day, many biased news about protests are shared. For this reason, CNN's news from May to August was examined.

First, the most used words were examined. The results of this research are as follows;

 [1] "after"          "also"           "and"            "are"            "arrested"       "been"           "bill"           "but"            "china"          "city"           "demonstrations"
[12] "extradition"    "for"            "from"           "had"            "has"            "have"           "hong"           "including"      "kong"           "last"           "long"       
[23] "monday"         "more"           "night"          "not"            "one"            "party"          "people"         "police"         "political"      "protest"        "protesters" 
[34] "protests"       "said"           "since"          "sunday"         "than"           "that"           "the"            "their"          "they"           "two"            "was"         
[45] "were"           "which"          "will"           "with"           "yuen"

Then, correlation analysis of some of the most commonly used words was examined. The first of the examined words is the China. The correlation analysis of the "China" word is as follows;

   mainland         mob         unlawful       station     between 
           0.98        0.98               0.98             0.97        0.96             
         movement      unrest                    accusations
               0.80        0.79                           0.76
   

As can be seen, one of the words with the highest correlation with China is unlawful. Then the correlation analysis of the word "police" was examined. The correlation analysis of the "police" word is as follows;

consecutive       march         weeks        people     kok          mong       
1.00                    1.00          1.00          0.96          0.92          0.92                 
demonstrators   extradition       support
          0.79          0.79                  0.79
       island         
         0.78       
As it is seen, the words that have the highest correlation with the word police are people, mong kok (one of the areas where protests continue) and arrest. Finally, the most commonly used words are shown in a graph.